applications
Mohammad Yazdani-Asrami3, Wenjuan Song1,3 and Zhenan Jiang1,2,4
1Propulsion, Electrification & Superconductivity Group, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, United Kingdom
2Paihau-Robinson Research Institute, Victoria University of Wellington, Lower Hutt 5046, New Zealand
Status
Conventional transformers are widely used in power systems or transportation applications (as traction transformers), and their performance greatly influences the reliability of the con- nected system. Although conventional transformers can reach an efficiency above 99%, many concerns still exist around their environmental footprint (CO2emission and oil hazards), safety (risks of explosion and fire), insulation (fatigue and fail- ure due to thermal stress), and reliability (due to produced gas in transformer oil). Superconducting transformers could address the above concerns and, in some occasions, could offer better efficiency and lighter weight and smaller size [111, 112], e.g. superconducting traction transformers (shown in figure20) could reduce system weight by half and achieve effi- ciency up to 99.5% from 94% which is the typical efficiency of oil-based conventional traction transformers [113].
Current and future challenges
Despite the advantages offered by superconducting trans- formers, some challenges need to be tackled within this dec- ade to highlight the figure of merits of superconducting trans- formers against their conventional counterparts. Some of these challenges are explained as follows:
(a) Purchasing price. Small- and medium-scale supercon- ducting transformers are not economically competitive with conventional ones yet, mainly due to extremely high purchasing prices, including the cost of the super- conducting tape/wire and cooling system. However, if the total ownership cost is considered, superconducting transformers, in some cases, would be cheaper than oil- immersed ones in the course of 35 yr of their lifetime [114].
(b) Weight and size. Weight and size reduction are always desirable for the transportation sector, including electric aircraft and high-speed train traction systems. Accurate purpose-based sizing of transformers should be implemen- ted to design a superconducting transformer with optimal geometry and optimised EM performance [113].
(c) Fault tolerance performance. Superconducting trans- formers provide intrinsic fault current limiting function due to the multi-layer structure of coated superconductors.
However, it is still vulnerable against short circuit faults longer than a couple of 100 ms without proper design, whilst a conventional one can tolerate short circuit faults for up to 2 s [115,116].
(d) Cooling cost.A cooling system is required to provide the cryogenic temperature for superconducting transformers, which will increase the total weight and reduce the total efficiency of the system due to its low efficiency capped by Carnot’s theory. Cryogenic cooling systems should be optimised and designed considering heat loads, operating temperature, and all other thermodynamic parameters such as pressure and flow rate if forced circulation cooling is adopted [117].
(e) Condition monitoring. Superconducting transformers require unique condition monitoring techniques due to the fragile multi-layer conductor structure and cryogenic working environment inside the cryostat. Therefore, non- destructive intelligent techniques are required, to not only detect any inter-turn faults in superconducting windings but also detect potential hotspots in superconductors before causing any disastrous damage [118]. In addi- tion, lifetime estimation models can be developed using AI techniques (e.g. based on ANNs) using the reliability, fault, and maintenance data, that essentially assist in better condition monitoring of superconducting transformers.
(f) Tape/wire performance and manufacturing challenges.
It is still challenging for manufacturers to produce high- performance low-cost superconductors competitive with copper/aluminium wires. Keeping high critical current uniformity over long-length and having high in-field crit- ical current performance requires continuous improve- ment in manufacturing technology [119]. 2D homogen- eity of the critical current density of conductors is critical to avoid hotspots and produce high-quality Roebel cables [120]. In addition, the magnetic properties of the con- ductor substrate influence the AC loss characteristics of the superconductors when exposed to AC magnetic fields.
Advances in science and technology to meet challenges
AI techniques could be used to tackle the design, fabrica- tion, and manufacturing challenges of superconducting trans- formers, as shown in figure21.
Multi-objective AI optimisation reduces size, mass, and cost by finding the optimal geometry/design of a supercon- ducting transformer to satisfy constraints such as AC loss, effi- ciency, total weight, initial price, voltage regulation, fault per- formance, etc. Superconducting transformer parameters, such as the size and material of the iron core, tape, coil wind- ing, flux diverters, and cryostat can be the outcome of such optimisation problems. AI together with transformative man- ufacturing techniques such as additive manufacturing, will lead to rapid prototyping and efficient manufacturing, espe- cially for the insulation, winding former, and cryostat. The optimal design of the cooling system can be achieved by
Figure 20. Comparison between a conventional and a superconducting traction transformer for Chinese Fuxing high-speed train (note that the right figure shows suggested positioning of each component for the superconducting traction transformer in the standard space (4.036 m×2.4 m×0.735 m)).
Figure 21. AI techniques can help to address challenges of superconducting transformers.
solving an optimisation problem considering EM and ther- modynamic parameters, such as winding heat load, heat leak- age, heat transfer coefficients, pressure, LN2 flow rate, pres- sure drop, operating temperature, etc. The constraints could include the cooling curves of generative model (GM) and Stirl- ing cryocoolers. The optimisation outcomes/results would be optimal values for the number and type of cold heads, pres- sure, and flow rate. The minimum cost or the maximum effi- ciency of the cooling system could be obtained as well. For such optimisation problems, meta-heuristic algorithms, evol- utionary algorithms, or bio-inspired techniques can be used,
including particle swarm optimisation, grey wolf optimisation, and firefly optimisation, among others.
Fault and recovery performances of superconducting trans- formers can be dramatically improved by a multi-objective optimisation problem to find the optimal thickness, and best material composition of different layers (substrate, supercon- ductor, buffers, and stabiliser layers) to meet a specific fault impedance. Note that the electro-thermal parameters of each material candidate are temperature and magnetic field depend- ent. AI is able to consider all these interdependencies simultan- eously. Constraints can be specific heat transfer, desired fault
impedance, specified recovery time, and cost. Meta-heuristic algorithms as mentioned above, are the best option for such a study.
Non-destructive condition monitoring methods for super- conducting transformers must be developed in the near future to detect inter-turn faults, hot spots, and deformation in wind- ings. Traditional relay-based protection systems are sensitive to large external short circuit currents but cannot detect inter- turn faults for superconducting transformers in the early stages of fault development and this can be catastrophic if the fault lasts long. AI techniques can detect inter-turn faults in a super- conducting winding by comparing the time and/or frequency domain data of some faulty and healthy samples of transformer current. Fibre optic sensor is currently used to detect hot spots of superconducting windings, which its implementation adds the complexity of the winding assembly process and changes the heat transfer of LN2 near the winding. AI techniques can detect the hot spot by analysing the current and voltage wave- forms of the windings. Sufficient experimental data on the crit- ical current of an intentionally damaged tape are necessary.
This is a classification and clustering task for AI techniques, which can be done through different ML approaches. In addi- tion, if real-time detection is desired, DL approaches which use CNNs can be used as very efficient options.
Intelligent simulation models of superconducting trans- formers can be established based on surrogate or meta- modelling methods. The existing modelling/simulation is performed through analytical, equivalent circuit-based, or FE-based models that are incapable of offering real-time ana- lysis. AI-based meta-models composed of multilayer NNs could achieve fast computation and acceptable accuracy com- pared to other models. For instance, once a meta-model of a transformer is established, online AC loss estimation/predic- tion is accessed by logging the input current and voltage of
windings. Any drastic drift of AC loss from the base value would indicate an anomaly in transformer winding, e.g. early quench, hot spot, critical current degradation, etc.
Real-time intelligent quality monitoring of superconductor production lines can be designed to analyse the output data of the sensors. ML and image processing techniques will help find important parameters to produce superconducting tape/wire with high uniformity of critical current density along the length. ML methods can be adopted to predict the critical temperature of new superconductors.
Concluding remarks
AI techniques can address and tackle the challenges that a superconducting transformer is confronted with, i.e. purchas- ing price, weight and size, fault tolerance performance, cool- ing cost, condition monitoring, and tape performance and manufacturing challenges. The opportunities offered by AI can lead to producing a smart superconducting transformer in the next decades. Many AI techniques including those of heuristic and meta-heuristic optimisation algorithms, ANNs, deep NNs, etc can be used to address the aforementioned challenges.
Acknowledgments
This work was supported in part by the New Zealand Ministry of Business, Innovation and Employment (MBIE) by the Stra- tegic Science Investment Fund “Advanced Energy Technology Platforms” under Contract RTVU2004.
Networking support provided by the European Coopera- tion in Science and Technology, COST Action CA19108 (Hi- SCALE) is acknowledged.
14. AI and BD for improvement of the